Today Big Data

Truly, Big data today means big indeed. It punctures the lives of people on a daily basis. It is used in almost all industries all over the world.

Basically, big data refers to the enormous amount of information that organizations build and collect. Storing and processing Big Data is a decades-old practice. With modern analytics, the deep pool of data has become an immensely powerful tool today.

Big Data today means big indeed. There are four major factors that contribute to the importance of big data these days. These include:

  • Exponential smart phone adoption
  • Data volumes tsunami
  • Connected culture
  • Surge of online opinions and social media

Whether people are aware of it or not, big data punctures daily lives.  And although data often is used in a sales context, its uses and relevance go far beyond sales. The biggest eCommerce platform in the world, Alibaba has used customer data to venture towards the small business loan market. Beyond business-oriented apps, data can be used to save lives. Medical practitioners and hospitals are increasingly using intelligent devices for capturing data from patients, with the growth of connected devices. In any sector and industry, big data plays an integral role these days.

Today, enterprises have huge opportunities of harnessing big data to boost competitiveness. In the new age of the Internet of Things or IoT, data and information in organizations come from multiple different sources all over the world. Everyone and everything is connected to everyone and everything through a device and through data. To meet the challenge in big data, a new data management framework is required. A hybrid, comprehensive approach of managing big data. To be comprehensive, the approach should encompass three pillars needed for management solutions, including data integration, governance, data quality and security.

Big Data is big business. Furthermore, capitalizing on it needs a step up for most organizations. But, technology is here and innovation continue to augment storage solutions capacity rapidly. New ways of lining data sets played a huge role in the generation of new insights. Creative approaches of data visualization, often prove integral to the process of knowledge creation.


There are five large ways wherein using Big Data could create value.

  1. Big data could unlock significant value via making transparent information and usable at a much higher frequency.
  2. Organizations build a store more transactional data in digital form, they could gather more detailed and accurate performance info on everything from product inventories to sick days, and thus boost performance and expose variability. Leading companies use data collection and analysis to do controlled experiments for better management decisions.
  3. Big Data enables ever-narrower segmentation of customers and thus more precisely tailored services or products.
  4. Sophisticated analytics could improve decision-making substantially.
  5. Big data could be used to enhance the development of next generation services and products.

Using Big data becomes a key basis of growth and competition for individual organizations. From the standpoint of competitiveness and possible value capture, all organizations have to take big data seriously. In most industries, new entrants and established competitors alike would leverage data-driven strategies to compete, innovate and capture value from deep and up-to-real-time information.


1. Big Data architect. Data architects build blueprints for data management systems. The job is to assess both internal and external data and designing a way of merging and organizing data.

2. Bid Data engineer. The job of the data engineer is to work with companies in developing, maintaining, testing and evaluating big data solutions. Google is in need of big data engineers.

3. Data Architect engineer. The work combines the skills in designing and implementing data management systems. Version, a big wireless carrier, wants someone to take care of its massive flow of data. The architect engineer handles technical solutions, data architecture flow and drive the team on implementing numerous complex projects.

4. IT Data scientist. The scientist spearheads data service and analytics service and should have expertise in quantitative analysis, analytics, data visualization techniques and modeling to find solutions for various technical and business requirements.

5. Global data analytics. The person should be able to employ meaningful visualization of data to the customers via sustainable reporting solutions.

6. Data analytics engineer. This is a step up from data scientist. It is a good fit for someone who could oversee and process data flow from the source to the destination.
No doubt that the future of big data is today. The massive use of big data in almost all industries make it one of the best options for organizations everywhere.



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